# RapidAI4EO: A Corpus for Higher Spatial and Temporal Reasoning

arXiv: https://arxiv.org/abs/2110.01919 · HTML: https://mlsanity.com/paper/2110.01919/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 11 references that are in the map.
- First posted on arXiv 5 Oct 2021.

## Builds on (most cited first, 7 of 7)

- [EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification](https://mlsanity.com/paper/1709.00029/) (2017)
- [BigEarthNet: A Large-Scale Benchmark Archive For Remote Sensing Image Understanding](https://mlsanity.com/paper/1902.06148/) (2019)
- [Weakly Supervised Semantic Segmentation of Satellite Images for Land Cover Mapping -- Challenges and Opportunities](https://mlsanity.com/paper/2002.08254/) (2020)
- [Improving land cover segmentation across satellites using domain adaptation](https://mlsanity.com/paper/1912.05000/) (2019)
- [Semi-supervised Classification using Attention-based Regularization on Coarse-resolution Data](https://mlsanity.com/paper/2001.00994/) (2020)
- [Learning Disentangled Representations of Satellite Image Time Series](https://mlsanity.com/paper/1903.08863/) (2019)
- [Visual search over billions of aerial and satellite images](https://mlsanity.com/paper/2002.02624/) (2020)

## Built on by (most cited first, 5 of 5)

- [EarthNets: Empowering AI in Earth Observation](https://mlsanity.com/paper/2210.04936/) (2022)
- [Major TOM: Expandable Datasets for Earth Observation](https://mlsanity.com/paper/2402.12095/) (2024)
- [Deep Learning for Satellite Image Time Series Analysis: A Review](https://mlsanity.com/paper/2404.03936/) (2024)
- [FLAIR-HUB: Large-scale Multimodal Dataset for Land Cover and Crop Mapping](https://mlsanity.com/paper/2506.07080/) (2025)
- [RapidAI4EO: Mono- and Multi-temporal Deep Learning models for Updating the CORINE Land Cover Product](https://mlsanity.com/paper/2210.14624/) (2022)

## Abstract (arXiv)

Under the sponsorship of the European Union Horizon 2020 program, RapidAI4EO will establish the foundations for the next generation of Copernicus Land Monitoring Service (CLMS) products. The project aims to provide intensified monitoring of Land Use (LU), Land Cover (LC), and LU change at a much higher level of detail and temporal cadence than it is possible today. Focus is on disentangling phenology from structural change and in providing critical training data to drive advancement in the Copernicus community and ecosystem well beyond the lifetime of this project. To this end we are creating the densest spatiotemporal training sets ever by fusing open satellite data with Planet imagery at as many as 500,000 patch locations over Europe and delivering high resolution daily time series at all locations. We plan to open source these datasets for the benefit of the entire remote sensing community.